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Machine learning (ML) is becoming an effective tool for studying 2D materials. Taking as input computed or experimental materials data, ML algorithms predict the structural, electronic, mechanical, and chemical properties of 2D materials that have yet to be discovered. Such predictions expand investigations on how to synthesize 2D materials and use them in various applications, as well as greatly reduce the time and cost to discover and understand 2D materials. This tutorial review focuses on the understanding, discovery, and synthesis of 2D materials enabled by or benefiting from various ML techniques. We introduce the most recent efforts to adopt ML in various fields of study regarding 2D materials and provide an outlook for future research opportunities. The adoption of ML is anticipated to accelerate and transform the study of 2D materials and their heterostructures. 

Abstract Two‐dimensional (2D) antiferromagnetic (AFM) semiconductors are promising components of opto‐spintronic devices due to terahertz operation frequencies and minimal interactions with stray fields. However, the lack of net magnetization significantly limits the number of experimental techniques available to study the relationship between magnetic order and semiconducting properties. Here, they demonstrate conditions under which photocurrent spectroscopy can be employed to study many‐body magnetic excitons in the 2D AFM semiconductor NiI₂. The use of photocurrent spectroscopy enables the detection of optically dark magnetic excitons down to bilayer thickness, revealing a high degree of linear polarization that is coupled to the underlying helical AFM order of NiI₂. In addition to probing the coupling between magnetic order and dark excitons, this work provides strong evidence for the multiferroicity of NiI₂down to bilayer thickness, thus demonstrating the utility of photocurrent spectroscopy for revealing subtle opto‐spintronic phenomena in the atomically thin limit.